The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.
quantreg.nonpar, quantreg, QuantifQuantile, quantregGrowth, fda
Environmetrics, Econometrics, Optimization, ReproducibleResearch, Robust, SocialSciences, Survival
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For attribution, please cite this work as
Lipsitz, et al., "The R Journal: quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression", The R Journal, 2016
BibTeX citation
@article{RJ-2016-052,
author = {Lipsitz, Michael and Belloni, Alexandre and Chernozhukov, Victor and Fernández-Val, Iván},
title = {The R Journal: quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-052},
doi = {10.32614/RJ-2016-052},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {370-381}
}